Major
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Major | Gender | Career | Ethnicity | First Generation Students | Dependents | Medical Condition | Medical Care | Analysis
Function
To begin, let’s explore the function we created to run all of this statistical analysis:
mysubsetMDS <- function(x){
mysubset <- df %>%
select(starts_with(x))
meta <- metaMDS(mysubset)
MDS_df <- data.frame(MDS1=meta$points[,1],MDS2=meta$points[,2]) %>%
cbind(demo)
return(MDS_df)
}
In English, this function allows us to run the MDS according to each subset of demographic and each subset of question type that we want. Obviously, in this page, we are exploring Majors.
Question Types
Science Identity
Let’s explore the science identity subset of questions first. Running our function we created and plotting it, we are left with this image of the plot:
This is great and all, but let’s run an adonis test to see if there is a significant difference in how different majors responded to science identity questions:
##
## Call:
## adonis(formula = si ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.8771 0.109636 3.6695 0.08833 0.002 **
## Residuals 303 9.0528 0.029877 0.91167
## Total 311 9.9299 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Look at that! It was significant!
Career Motivation
Next, let’s look into Carer Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = cm ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.4423 0.055290 1.0885 0.02794 0.336
## Residuals 303 15.3905 0.050794 0.97206
## Total 311 15.8329 1.00000This shows that the differences in answers are not significant, according to major.
Intrinsic Motivation
Now, Intrinsic Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = im ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.3414 0.042676 1.5266 0.03874 0.149
## Residuals 303 8.4705 0.027955 0.96126
## Total 311 8.8119 1.00000This shows that the differences in answers are not significant, according to major.
Self-Determination
Now, Self-Determination:
And an Adonis test:
##
## Call:
## adonis(formula = sd ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.1709 0.021358 0.86911 0.02243 0.514
## Residuals 303 7.4460 0.024574 0.97757
## Total 311 7.6168 1.00000This shows that the differences in answers are not significant, according to major.
Self-Efficacy
Now, Self-Efficacy:
And an Adonis test:
##
## Call:
## adonis(formula = se ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.3163 0.039543 1.463 0.03719 0.169
## Residuals 303 8.1896 0.027028 0.96281
## Total 311 8.5059 1.00000This shows that the differences in answers are not significant, according to major.
Grade Motivation
Now, Grade Motivation:
And an Adonis test:
##
## Call:
## adonis(formula = gm ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.0883 0.011043 0.49189 0.01282 0.803
## Residuals 303 6.8025 0.022451 0.98718
## Total 311 6.8909 1.00000This shows that the differences in answers are not significant, according to major.
Competency in Science
Now, Competency in Science:
And an Adonis test:
##
## Call:
## adonis(formula = sci_comp ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.3478 0.043476 1.2599 0.03219 0.208
## Residuals 303 10.4562 0.034509 0.96781
## Total 311 10.8040 1.00000This shows that the differences in answers are not significant, according to major.
Personal Community Orientation
Now, Personal Community Orientation:
And an Adonis test:
##
## Call:
## adonis(formula = per_comm_orient ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.10277 0.0128465 1.4734 0.03745 0.108
## Residuals 303 2.64181 0.0087188 0.96255
## Total 311 2.74458 1.00000This shows that the differences in answers are not significant, according to major.
Science Community Orientation
Now, Competency in Science:
Well crap, something is broke there. Let’s try running a different form of MDS, giving us a similar analysis in a different way. We can interpret this data similarly to how we did before:
And an Adonis test:
##
## Call:
## adonis(formula = sci_comm_orient ~ demo$major)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## demo$major 8 0.0980 0.012255 0.82608 0.02135 0.511
## Residuals 303 4.4949 0.014835 0.97865
## Total 311 4.5929 1.00000This shows that the differences in answers are not significant, according to major.
Conclusion
Ultimately, what we can understand from all of this is that students significantly answer Science Identity questions differently according to their major.
In summary, let’s take a look at the running list we are making of which questions were significant and not according to demographics:
